Identifying The Dimensions, Components, and Indicators of Hyper-Personalized Education Using Artificial Intelligence

The present study was conducted with the aim of identifying the dimensions, components, and indicators of hyper-Personalized education using artificial intelligence. The type of research was qualitative in terms of data type, which included meta synthesis and Delphi stages in terms of nature. The statistical population in the qualitative section and meta synthesis stage included all theoretical foundations and relevant background from external databases, and in the Delphi stage, ۱۵ participants (experts) were selected using purposive non-random sampling. The data collection method in the meta synthesis stage was a systematic literature review, and in the Delphi stage, a worksheet, and validity and reliability were examined, and the results indicated that the research tools were valid and reliable. The data analysis method in the meta synthesis stage was systematic analysis, and in the Delphi stage, the Kendall agreement coefficient was used with Maxqda-V۲۰۱۸ and Spss-V۲۳ software. The findings showed that hyper-Personalized education using artificial intelligence included a cognitive dimension with the components of prior knowledge level analysis (۵ indicators), individual learning style (۶ indicators), cognitive adaptation (۶ indicators), memory and memorization (۶ indicators), and problem solving and critical thinking (۶ indicators); an affective dimension with the components of identifying emotional states (۶ indicators), intrinsic motivation (۶ indicators), learner self-efficacy (۶ indicators), learner satisfaction (۶ indicators), and emotional involvement in learning (۶ indicators); a behavioral dimension with the components of interaction patterns with the system (۶ indicators), participation in group activities (۶ indicators), learning discipline and pursuit (۵ indicators), knowledge seeking behaviors (۶ indicators), and interaction with feedback (۶ indicators); and a contextual dimension with the components of learning environmental conditions (۶ indicators), technology and tools used (۶ indicators), cultural and linguistic adaptation (۶ indicators), educational access and justice (۶ indicators), and learning data analysis (۶ indicators).

کلیدواژه ها:

Hyper-Personalized Education ، Use of Artificial Intelligence ، Individual Learning